Overview

Dataset statistics

Number of variables29
Number of observations59824
Missing cells193408
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 MiB
Average record size in memory232.0 B

Variable types

NUM17
CAT10
UNSUPPORTED1
BOOL1

Warnings

amenities has a high cardinality: 51780 distinct values High cardinality
city has a high cardinality: 133 distinct values High cardinality
name has a high cardinality: 58689 distinct values High cardinality
zipcode has a high cardinality: 474 distinct values High cardinality
state is highly correlated with metropolitanHigh correlation
metropolitan is highly correlated with stateHigh correlation
has_availability has 59824 (100.0%) missing values Missing
review_scores_checkin has 14346 (24.0%) missing values Missing
review_scores_cleanliness has 14280 (23.9%) missing values Missing
review_scores_communication has 14285 (23.9%) missing values Missing
review_scores_location has 14349 (24.0%) missing values Missing
review_scores_rating has 14200 (23.7%) missing values Missing
review_scores_value has 14354 (24.0%) missing values Missing
weekly_price has 46386 (77.5%) missing values Missing
zipcode has 826 (1.4%) missing values Missing
name is uniformly distributed Uniform
id has unique values Unique
latitude has unique values Unique
longitude has unique values Unique
has_availability is an unsupported type, check if it needs cleaning or further analysis Unsupported
availability_30 has 24034 (40.2%) zeros Zeros
bedrooms has 4964 (8.3%) zeros Zeros

Reproduction

Analysis started2020-11-25 01:58:59.661934
Analysis finished2020-11-25 01:59:45.941244
Duration46.28 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

accommodates
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing61
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.031959574
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:46.009572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.999426632
Coefficient of variation (CV)0.659450294
Kurtosis7.884614679
Mean3.031959574
Median Absolute Deviation (MAD)1
Skewness2.251525003
Sum181199
Variance3.997706857
MonotocityNot monotonic
2020-11-24T20:59:46.099620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
22653144.3%
 
4966516.2%
 
1778713.0%
 
3653810.9%
 
635215.9%
 
527194.5%
 
812182.0%
 
76291.1%
 
104590.8%
 
91890.3%
 
Other values (6)5070.8%
 
ValueCountFrequency (%) 
1778713.0%
 
22653144.3%
 
3653810.9%
 
4966516.2%
 
527194.5%
 
ValueCountFrequency (%) 
161640.3%
 
15310.1%
 
14530.1%
 
1328< 0.1%
 
121810.3%
 

amenities
Categorical

HIGH CARDINALITY

Distinct51780
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
{}
 
383
{"translation missing: en.hosting_amenity_49","translation missing: en.hosting_amenity_50"}
 
98
{"Family/kid friendly"}
 
55
{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,"Buzzer/wireless intercom",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector",Essentials,Shampoo,"24-hour check-in",Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}
 
36
{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,Heating}
 
31
Other values (51775)
59221 
ValueCountFrequency (%) 
{}3830.6%
 
{"translation missing: en.hosting_amenity_49","translation missing: en.hosting_amenity_50"}980.2%
 
{"Family/kid friendly"}550.1%
 
{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,"Buzzer/wireless intercom",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector",Essentials,Shampoo,"24-hour check-in",Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}360.1%
 
{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,Heating}310.1%
 
{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,Heating}28< 0.1%
 
{Internet,"Wireless Internet","Air conditioning",Kitchen,Heating}28< 0.1%
 
{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,Heating,Essentials}27< 0.1%
 
{Internet,"Wireless Internet","Air conditioning",Kitchen,Heating,Essentials}27< 0.1%
 
{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,"Buzzer/wireless intercom",Heating}26< 0.1%
 
Other values (51770)5908598.8%
 
2020-11-24T20:59:46.357486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique47974 ?
Unique (%)80.2%
2020-11-24T20:59:46.495030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1142
Median length250
Mean length249.9386701
Min length2

availability_30
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.97644758
Minimum0
Maximum30
Zeros24034
Zeros (%)40.2%
Memory size467.4 KiB
2020-11-24T20:59:46.601484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q313
95-th percentile29
Maximum30
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.06264069
Coefficient of variation (CV)1.261544138
Kurtosis-0.2423865862
Mean7.97644758
Median Absolute Deviation (MAD)3
Skewness1.07996121
Sum477183
Variance101.2567376
MonotocityNot monotonic
2020-11-24T20:59:46.695516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
02403440.2%
 
3029194.9%
 
123884.0%
 
222543.8%
 
2921973.7%
 
320953.5%
 
420163.4%
 
518513.1%
 
616552.8%
 
715722.6%
 
Other values (21)1684328.2%
 
ValueCountFrequency (%) 
02403440.2%
 
123884.0%
 
222543.8%
 
320953.5%
 
420163.4%
 
ValueCountFrequency (%) 
3029194.9%
 
2921973.7%
 
288881.5%
 
277151.2%
 
264870.8%
 

bathrooms
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing202
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.183447385
Minimum0
Maximum8
Zeros145
Zeros (%)0.2%
Memory size467.4 KiB
2020-11-24T20:59:46.791046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4837264537
Coefficient of variation (CV)0.4087435232
Kurtosis23.41188858
Mean1.183447385
Median Absolute Deviation (MAD)0
Skewness3.694585316
Sum70559.5
Variance0.233991282
MonotocityNot monotonic
2020-11-24T20:59:46.877666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
14877981.5%
 
254789.2%
 
1.530215.0%
 
2.510911.8%
 
35000.8%
 
3.52670.4%
 
01450.2%
 
41220.2%
 
0.51130.2%
 
4.5360.1%
 
Other values (6)700.1%
 
(Missing)2020.3%
 
ValueCountFrequency (%) 
01450.2%
 
0.51130.2%
 
14877981.5%
 
1.530215.0%
 
254789.2%
 
ValueCountFrequency (%) 
816< 0.1%
 
71< 0.1%
 
6.53< 0.1%
 
615< 0.1%
 
5.510< 0.1%
 

bed_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
Real Bed
57988 
Futon
 
669
Pull-out Sofa
 
544
Airbed
 
431
Couch
 
192
ValueCountFrequency (%) 
Real Bed5798896.9%
 
Futon6691.1%
 
Pull-out Sofa5440.9%
 
Airbed4310.7%
 
Couch1920.3%
 
2020-11-24T20:59:46.981211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-24T20:59:47.047544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:47.137867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length8
Mean length7.987881118
Min length5

bedrooms
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.221969228
Minimum0
Maximum10
Zeros4964
Zeros (%)8.3%
Memory size467.4 KiB
2020-11-24T20:59:47.225021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7759823495
Coefficient of variation (CV)0.6350260971
Kurtosis8.27732671
Mean1.221969228
Median Absolute Deviation (MAD)0
Skewness2.03348828
Sum72987
Variance0.6021486067
MonotocityNot monotonic
2020-11-24T20:59:47.311090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
14210870.4%
 
2865514.5%
 
049648.3%
 
329014.8%
 
47951.3%
 
52150.4%
 
6580.1%
 
714< 0.1%
 
811< 0.1%
 
105< 0.1%
 
(Missing)950.2%
 
ValueCountFrequency (%) 
049648.3%
 
14210870.4%
 
2865514.5%
 
329014.8%
 
47951.3%
 
ValueCountFrequency (%) 
105< 0.1%
 
93< 0.1%
 
811< 0.1%
 
714< 0.1%
 
6580.1%
 

beds
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing109
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.626107343
Minimum0
Maximum16
Zeros4
Zeros (%)< 0.1%
Memory size467.4 KiB
2020-11-24T20:59:47.397674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.128177847
Coefficient of variation (CV)0.6937905123
Kurtosis18.65943971
Mean1.626107343
Median Absolute Deviation (MAD)0
Skewness3.270074892
Sum97103
Variance1.272785254
MonotocityNot monotonic
2020-11-24T20:59:47.485021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
13794063.4%
 
21334422.3%
 
347307.9%
 
420643.5%
 
58151.4%
 
64330.7%
 
71380.2%
 
81070.2%
 
10480.1%
 
9350.1%
 
Other values (7)610.1%
 
(Missing)1090.2%
 
ValueCountFrequency (%) 
04< 0.1%
 
13794063.4%
 
21334422.3%
 
347307.9%
 
420643.5%
 
ValueCountFrequency (%) 
1612< 0.1%
 
153< 0.1%
 
144< 0.1%
 
136< 0.1%
 
1216< 0.1%
 
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
strict
25278 
flexible
19299 
moderate
15072 
super_strict_30
 
172
no_refunds
 
1
Other values (2)
 
2
ValueCountFrequency (%) 
strict2527842.3%
 
flexible1929932.3%
 
moderate1507225.2%
 
super_strict_301720.3%
 
no_refunds1< 0.1%
 
long_term1< 0.1%
 
super_strict_601< 0.1%
 
2020-11-24T20:59:47.589782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-11-24T20:59:47.655407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:47.753426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length8
Mean length7.175213961
Min length6

city
Categorical

HIGH CARDINALITY

Distinct133
Distinct (%)0.2%
Missing46
Missing (%)0.1%
Memory size467.4 KiB
new york
19566 
brooklyn
16507 
washington
7741 
chicago
5194 
boston
3388 
Other values (128)
7382 
ValueCountFrequency (%) 
new york1956632.7%
 
brooklyn1650727.6%
 
washington774112.9%
 
chicago51948.7%
 
boston33885.7%
 
queens26924.5%
 
denver24984.2%
 
sunnysidebronx6601.1%
 
staten island2530.4%
 
astoria2380.4%
 
Other values (123)10411.7%
 
2020-11-24T20:59:47.871533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique44 ?
Unique (%)0.1%
2020-11-24T20:59:47.993513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length8
Mean length8.014057903
Min length2

has_availability
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing59824
Missing (%)100.0%
Memory size467.5 KiB

host_id
Real number (ℝ≥0)

Distinct47743
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31006954.44
Minimum72
Maximum129710473
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:48.114219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile505080.3
Q15453285.25
median19655896
Q346679319.25
95-th percentile104113408
Maximum129710473
Range129710401
Interquartile range (IQR)41226034

Descriptive statistics

Standard deviation32167333.99
Coefficient of variation (CV)1.037423203
Kurtosis0.6024662294
Mean31006954.44
Median Absolute Deviation (MAD)16496225.5
Skewness1.224225418
Sum1.854960042e+12
Variance1.034737376e+15
MonotocityNot monotonic
2020-11-24T20:59:48.236327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
302835942280.4%
 
251881580.3%
 
122430511150.2%
 
9419684990.2%
 
4962900560.1%
 
22348222500.1%
 
3965428450.1%
 
8160186440.1%
 
46630199420.1%
 
33127842380.1%
 
Other values (47733)5894998.5%
 
ValueCountFrequency (%) 
721< 0.1%
 
2831< 0.1%
 
5241< 0.1%
 
6251< 0.1%
 
6511< 0.1%
 
ValueCountFrequency (%) 
1297104731< 0.1%
 
1296380701< 0.1%
 
1296009951< 0.1%
 
1294749891< 0.1%
 
1293328231< 0.1%
 

id
Real number (ℝ≥0)

UNIQUE

Distinct59824
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10071710.51
Minimum590
Maximum18670793
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:48.371946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum590
5-th percentile748367
Q15477726
median10227927.5
Q315057850.5
95-th percentile17796455.7
Maximum18670793
Range18670203
Interquartile range (IQR)9580124.5

Descriptive statistics

Standard deviation5507014.656
Coefficient of variation (CV)0.546780475
Kurtosis-1.185812616
Mean10071710.51
Median Absolute Deviation (MAD)4810926.5
Skewness-0.2351024259
Sum6.025300094e+11
Variance3.032721042e+13
MonotocityNot monotonic
2020-11-24T20:59:48.486180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
116674551< 0.1%
 
98254481< 0.1%
 
27987871< 0.1%
 
121069461< 0.1%
 
93933431< 0.1%
 
143720281< 0.1%
 
167169831< 0.1%
 
148840221< 0.1%
 
667411< 0.1%
 
127827721< 0.1%
 
Other values (59814)59814> 99.9%
 
ValueCountFrequency (%) 
5901< 0.1%
 
5921< 0.1%
 
6861< 0.1%
 
9301< 0.1%
 
12351< 0.1%
 
ValueCountFrequency (%) 
186707931< 0.1%
 
186691801< 0.1%
 
186689291< 0.1%
 
186681361< 0.1%
 
186681051< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
f
47009 
t
12815 
ValueCountFrequency (%) 
f4700978.6%
 
t1281521.4%
 
2020-11-24T20:59:48.566664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

latitude
Real number (ℝ≥0)

UNIQUE

Distinct59824
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.65136193
Minimum38.82340343
Maximum42.38998168
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:48.656595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum38.82340343
5-th percentile38.90649017
Q140.67384822
median40.72213316
Q340.79182043
95-th percentile42.31594424
Maximum42.38998168
Range3.566578254
Interquartile range (IQR)0.117972203

Descriptive statistics

Standard deviation0.8620897317
Coefficient of variation (CV)0.02120690896
Kurtosis0.4889390524
Mean40.65136193
Median Absolute Deviation (MAD)0.05706147595
Skewness-0.3961068842
Sum2431927.076
Variance0.7431987054
MonotocityNot monotonic
2020-11-24T20:59:48.768473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
42.364944981< 0.1%
 
40.70700661< 0.1%
 
42.334774641< 0.1%
 
40.745704641< 0.1%
 
40.577912761< 0.1%
 
41.944157541< 0.1%
 
40.807575411< 0.1%
 
40.718125031< 0.1%
 
41.914233421< 0.1%
 
40.731962531< 0.1%
 
Other values (59814)59814> 99.9%
 
ValueCountFrequency (%) 
38.823403431< 0.1%
 
38.823407661< 0.1%
 
38.827210181< 0.1%
 
38.82745061< 0.1%
 
38.828052481< 0.1%
 
ValueCountFrequency (%) 
42.389981681< 0.1%
 
42.38989751< 0.1%
 
42.389828271< 0.1%
 
42.389681731< 0.1%
 
42.389530631< 0.1%
 

longitude
Real number (ℝ)

UNIQUE

Distinct59824
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-76.67488126
Minimum-105.0945635
Maximum-71.00009992
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:48.905010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-105.0945635
5-th percentile-87.71004057
Q1-76.98172548
median-73.97298437
Q3-73.9427197
95-th percentile-71.11697839
Maximum-71.00009992
Range34.09446357
Interquartile range (IQR)3.039005775

Descriptive statistics

Standard deviation7.139988924
Coefficient of variation (CV)-0.09312031275
Kurtosis7.866628444
Mean-76.67488126
Median Absolute Deviation (MAD)0.03378550895
Skewness-2.859506855
Sum-4586998.097
Variance50.97944183
MonotocityNot monotonic
2020-11-24T20:59:49.022560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-73.911856621< 0.1%
 
-73.937455681< 0.1%
 
-87.705025051< 0.1%
 
-73.971997661< 0.1%
 
-73.979251941< 0.1%
 
-73.985282221< 0.1%
 
-73.929601731< 0.1%
 
-73.953668621< 0.1%
 
-87.688223351< 0.1%
 
-73.938294151< 0.1%
 
Other values (59814)59814> 99.9%
 
ValueCountFrequency (%) 
-105.09456351< 0.1%
 
-105.0920011< 0.1%
 
-105.09181641< 0.1%
 
-105.08842511< 0.1%
 
-105.08821931< 0.1%
 
ValueCountFrequency (%) 
-71.000099921< 0.1%
 
-71.000260541< 0.1%
 
-71.00033181< 0.1%
 
-71.000381391< 0.1%
 
-71.000741211< 0.1%
 

metropolitan
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
NYC
40740 
dc
7787 
chicago
5207 
boston
 
3585
denver
 
2505
ValueCountFrequency (%) 
NYC4074068.1%
 
dc778713.0%
 
chicago52078.7%
 
boston35856.0%
 
denver25054.2%
 
2020-11-24T20:59:49.144524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-24T20:59:49.212816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:49.294373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.523385263
Min length2

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct58689
Distinct (%)98.2%
Missing41
Missing (%)0.1%
Memory size467.4 KiB
DRIFTER INN Rockaway Beach
 
12
Loft Suite @ The Box House Hotel
 
11
Private room
 
11
Private room in Brooklyn
 
10
East Village Studio
 
9
Other values (58684)
59730 
ValueCountFrequency (%) 
DRIFTER INN Rockaway Beach12< 0.1%
 
Loft Suite @ The Box House Hotel11< 0.1%
 
Private room11< 0.1%
 
Private room in Brooklyn10< 0.1%
 
East Village Studio9< 0.1%
 
Private room in Williamsburg8< 0.1%
 
Lux 2BR by Fenway w/WiFi8< 0.1%
 
CLEAN NEW YORK APT at Central Park8< 0.1%
 
Cozy Rooms7< 0.1%
 
Private Room in Williamsburg Loft7< 0.1%
 
Other values (58679)5969299.8%
 
(Missing)410.1%
 
2020-11-24T20:59:49.511835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique57896 ?
Unique (%)96.8%
2020-11-24T20:59:49.906299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length98
Median length34
Mean length34.23634327
Min length1

price
Real number (ℝ≥0)

Distinct697
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.0093775
Minimum0
Maximum10000
Zeros5
Zeros (%)< 0.1%
Memory size467.4 KiB
2020-11-24T20:59:50.027316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q170
median109
Q3176
95-th percentile400
Maximum10000
Range10000
Interquartile range (IQR)106

Descriptive statistics

Standard deviation236.200222
Coefficient of variation (CV)1.485448379
Kurtosis433.8907042
Mean159.0093775
Median Absolute Deviation (MAD)44
Skewness15.2275995
Sum9512577
Variance55790.54487
MonotocityNot monotonic
2020-11-24T20:59:50.145227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10024124.0%
 
15023754.0%
 
7518133.0%
 
6017132.9%
 
5016512.8%
 
20016072.7%
 
8015332.6%
 
6514702.5%
 
12514512.4%
 
7013822.3%
 
Other values (687)4241770.9%
 
ValueCountFrequency (%) 
05< 0.1%
 
31< 0.1%
 
1020< 0.1%
 
113< 0.1%
 
125< 0.1%
 
ValueCountFrequency (%) 
100002< 0.1%
 
99992< 0.1%
 
99981< 0.1%
 
96001< 0.1%
 
80001< 0.1%
 

property_type
Categorical

Distinct32
Distinct (%)0.1%
Missing4
Missing (%)< 0.1%
Memory size467.4 KiB
Apartment
46149 
House
8466 
Condominium
 
1723
Townhouse
 
1282
Loft
 
1109
Other values (27)
 
1091
ValueCountFrequency (%) 
Apartment4614977.1%
 
House846614.2%
 
Condominium17232.9%
 
Townhouse12822.1%
 
Loft11091.9%
 
Bed & Breakfast3390.6%
 
Other3000.5%
 
Guesthouse770.1%
 
Dorm720.1%
 
Timeshare430.1%
 
Other values (22)2600.4%
 
2020-11-24T20:59:50.262122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2020-11-24T20:59:50.364363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length8.404854239
Min length3

review_scores_checkin
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing14346
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean9.738554906
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:50.449360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6628862042
Coefficient of variation (CV)0.06806823092
Kurtosis31.56654559
Mean9.738554906
Median Absolute Deviation (MAD)0
Skewness-4.444429459
Sum442890
Variance0.4394181197
MonotocityNot monotonic
2020-11-24T20:59:50.532202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
103666261.3%
 
9696711.6%
 
812872.2%
 
62330.4%
 
72100.4%
 
4550.1%
 
2450.1%
 
518< 0.1%
 
31< 0.1%
 
(Missing)1434624.0%
 
ValueCountFrequency (%) 
2450.1%
 
31< 0.1%
 
4550.1%
 
518< 0.1%
 
62330.4%
 
ValueCountFrequency (%) 
103666261.3%
 
9696711.6%
 
812872.2%
 
72100.4%
 
62330.4%
 

review_scores_cleanliness
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing14280
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean9.289785702
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:50.621371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.066888909
Coefficient of variation (CV)0.1148453736
Kurtosis9.924981715
Mean9.289785702
Median Absolute Deviation (MAD)0
Skewness-2.530994196
Sum423094
Variance1.138251945
MonotocityNot monotonic
2020-11-24T20:59:50.702122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
102510542.0%
 
91327422.2%
 
847908.0%
 
711171.9%
 
68041.3%
 
41840.3%
 
21450.2%
 
51070.2%
 
318< 0.1%
 
(Missing)1428023.9%
 
ValueCountFrequency (%) 
21450.2%
 
318< 0.1%
 
41840.3%
 
51070.2%
 
68041.3%
 
ValueCountFrequency (%) 
102510542.0%
 
91327422.2%
 
847908.0%
 
711171.9%
 
68041.3%
 

review_scores_communication
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing14285
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean9.758624476
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:50.788658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6454399592
Coefficient of variation (CV)0.06614046486
Kurtosis34.08967804
Mean9.758624476
Median Absolute Deviation (MAD)0
Skewness-4.637572603
Sum444398
Variance0.4165927409
MonotocityNot monotonic
2020-11-24T20:59:50.872575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
103748462.7%
 
9626810.5%
 
812552.1%
 
72100.4%
 
62070.3%
 
4450.1%
 
2440.1%
 
524< 0.1%
 
32< 0.1%
 
(Missing)1428523.9%
 
ValueCountFrequency (%) 
2440.1%
 
32< 0.1%
 
4450.1%
 
524< 0.1%
 
62070.3%
 
ValueCountFrequency (%) 
103748462.7%
 
9626810.5%
 
812552.1%
 
72100.4%
 
62070.3%
 

review_scores_location
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing14349
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean9.463001649
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:50.961713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8189236679
Coefficient of variation (CV)0.08653952501
Kurtosis9.417358201
Mean9.463001649
Median Absolute Deviation (MAD)0
Skewness-2.288947515
Sum430330
Variance0.6706359739
MonotocityNot monotonic
2020-11-24T20:59:51.041892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
102760646.1%
 
91307921.9%
 
837866.3%
 
75260.9%
 
63520.6%
 
4550.1%
 
5370.1%
 
2300.1%
 
34< 0.1%
 
(Missing)1434924.0%
 
ValueCountFrequency (%) 
2300.1%
 
34< 0.1%
 
4550.1%
 
5370.1%
 
63520.6%
 
ValueCountFrequency (%) 
102760646.1%
 
91307921.9%
 
837866.3%
 
75260.9%
 
63520.6%
 

review_scores_rating
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)0.1%
Missing14200
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean93.47475013
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:51.148926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile80
Q190
median96
Q3100
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.240907966
Coefficient of variation (CV)0.08816186141
Kurtosis15.70236035
Mean93.47475013
Median Absolute Deviation (MAD)4
Skewness-2.9996797
Sum4264692
Variance67.9125641
MonotocityNot monotonic
2020-11-24T20:59:51.268613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1001271721.3%
 
9330665.1%
 
9630445.1%
 
9529554.9%
 
9829514.9%
 
9729214.9%
 
9025944.3%
 
8021313.6%
 
9420263.4%
 
9216732.8%
 
Other values (46)954616.0%
 
(Missing)1420023.7%
 
ValueCountFrequency (%) 
20690.1%
 
272< 0.1%
 
302< 0.1%
 
40960.2%
 
431< 0.1%
 
ValueCountFrequency (%) 
1001271721.3%
 
9915312.6%
 
9829514.9%
 
9729214.9%
 
9630445.1%
 

review_scores_value
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing14354
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean9.384891137
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:51.374514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8742559064
Coefficient of variation (CV)0.09315567902
Kurtosis13.21852094
Mean9.384891137
Median Absolute Deviation (MAD)0
Skewness-2.680956495
Sum426731
Variance0.7643233898
MonotocityNot monotonic
2020-11-24T20:59:51.454755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
102455741.0%
 
91635027.3%
 
833775.6%
 
64920.8%
 
74570.8%
 
41190.2%
 
2720.1%
 
5430.1%
 
33< 0.1%
 
(Missing)1435424.0%
 
ValueCountFrequency (%) 
2720.1%
 
33< 0.1%
 
41190.2%
 
5430.1%
 
64920.8%
 
ValueCountFrequency (%) 
102455741.0%
 
91635027.3%
 
833775.6%
 
74570.8%
 
64920.8%
 

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
Entire home/apt
32103 
Private room
26024 
Shared room
 
1697
ValueCountFrequency (%) 
Entire home/apt3210353.7%
 
Private room2602443.5%
 
Shared room16972.8%
 
2020-11-24T20:59:51.552382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-24T20:59:51.613444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:51.690545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length13.58150575
Min length11

state
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size467.4 KiB
NY
40738 
DC
7755 
IL
5207 
MA
 
3585
CO
 
2505
Other values (4)
 
34
ValueCountFrequency (%) 
NY4073868.1%
 
DC775513.0%
 
IL52078.7%
 
MA35856.0%
 
CO25054.2%
 
MD310.1%
 
MP1< 0.1%
 
VT1< 0.1%
 
NJ1< 0.1%
 
2020-11-24T20:59:51.794926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-11-24T20:59:51.868041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:51.971661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

weekly_price
Real number (ℝ≥0)

MISSING

Distinct949
Distinct (%)7.1%
Missing46386
Missing (%)77.5%
Infinite0
Infinite (%)0.0%
Mean856.4712011
Minimum70
Maximum14000
Zeros0
Zeros (%)0.0%
Memory size467.4 KiB
2020-11-24T20:59:52.065476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile295
Q1450
median675
Q31000
95-th percentile2000
Maximum14000
Range13930
Interquartile range (IQR)550

Descriptive statistics

Standard deviation712.8567769
Coefficient of variation (CV)0.8323184435
Kurtosis58.34743434
Mean856.4712011
Median Absolute Deviation (MAD)270
Skewness5.312020859
Sum11509260
Variance508164.7844
MonotocityNot monotonic
2020-11-24T20:59:52.175509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5006641.1%
 
6005570.9%
 
10005180.9%
 
4004880.8%
 
7004770.8%
 
4504130.7%
 
8004120.7%
 
3503760.6%
 
12003420.6%
 
3003250.5%
 
Other values (939)886614.8%
 
(Missing)4638677.5%
 
ValueCountFrequency (%) 
702< 0.1%
 
801< 0.1%
 
851< 0.1%
 
993< 0.1%
 
1003< 0.1%
 
ValueCountFrequency (%) 
140003< 0.1%
 
130001< 0.1%
 
120001< 0.1%
 
100004< 0.1%
 
91491< 0.1%
 

zipcode
Categorical

HIGH CARDINALITY
MISSING

Distinct474
Distinct (%)0.8%
Missing826
Missing (%)1.4%
Memory size467.4 KiB
11211
 
2043
11206
 
1405
11221
 
1371
20002
 
1270
20009
 
1270
Other values (469)
51639 
ValueCountFrequency (%) 
1121120433.4%
 
1120614052.3%
 
1122113712.3%
 
2000212702.1%
 
2000912702.1%
 
1000212412.1%
 
1121611621.9%
 
2000111481.9%
 
1000911431.9%
 
1123810541.8%
 
Other values (464)4589176.7%
 
2020-11-24T20:59:52.313152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique67 ?
Unique (%)0.1%
2020-11-24T20:59:52.422355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length5
Mean length5.326390746
Min length1

Interactions

2020-11-24T20:59:11.939397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.123552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.229072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.337502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.438289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.543090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.644788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.749639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.856212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:12.963868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.064138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.173102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.280849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.389836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.497958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.606174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.714348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.820384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:13.922260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.021954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.209529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.306493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.407810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.508107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.608952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.711837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.814994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:14.911807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.017309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.122257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.227273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.331342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.435930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.539856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.641784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.748789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.854131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:15.964125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:16.067313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:16.174094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:16.278324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:16.384844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:16.492974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-24T20:59:38.847055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:38.960363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.072753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.184220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.296279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.407504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.518579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.632184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.738997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.849393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:39.952864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.061113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.167534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.275090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.384668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.493778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.797086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:40.918235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.041356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.153688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.265808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.377690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.488808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.598399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.703895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.807409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:41.915166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.015258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.119923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.224736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.330233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.437082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.543100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.643372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.751850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.860254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:42.974208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:43.082834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:43.191205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:43.299313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-24T20:59:52.519163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-24T20:59:52.733414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-24T20:59:52.948783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-24T20:59:53.171779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-24T20:59:53.382210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-24T20:59:43.712349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:44.797895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:45.376182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-24T20:59:45.658280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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02.0{"Cable TV","Wireless Internet","Air conditioning","Free parking on premises",Breakfast,"Pets live on this property",Cat(s),"Indoor fireplace",Heating,"Smoke detector","Carbon monoxide detector","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"translation missing: en.hosting_amenity_49","translation missing: en.hosting_amenity_50"}241.0Real Bed1.01.0moderatesunnysidebronxNaN1194457949480f40.852054-73.788680NYCCity Island Sanctuary relaxing BR & Bath w Parking99.0House10.010.010.010.0100.010.0Private roomNYNaN10464
14.0{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","First aid kit","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron}301.0Real Bed1.01.0flexiblesunnysidebronxNaN911797516042478t40.853491-73.788607NYCWATERFRONT STUDIO APARTMENT200.0ApartmentNaNNaNNaNNaNNaNNaNPrivate roomNYNaN10464
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33.0{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises","Buzzer/wireless intercom",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector",Essentials,Shampoo,Hangers,"Hair dryer",Iron}81.0Real Bed1.01.0strictlong island cityNaN138865106627449f40.849775-73.786609NYCLarge 1 BDRM in Great location125.0Apartment10.010.010.010.093.010.0Entire home/aptNY775.010464
44.0{Internet,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises","Pets allowed",Gym,Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","First aid kit",Essentials,"Laptop friendly workspace","translation missing: en.hosting_amenity_50"}171.0Real Bed1.01.0moderatesunnysidebronxNaN288115425557381t40.850024-73.789328NYCQuaint City Island Home69.0House10.010.010.010.097.010.0Private roomNY350.010464
52.0{TV,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector","Safety card",Essentials,Shampoo,"24-hour check-in",Hangers,"Hair dryer",Iron,"Laptop friendly workspace","Self Check-In",Lockbox}231.0Real Bed0.01.0moderatesunnysidebronxNaN4030329147025f40.844870-73.789541NYCCozy City Island Cottage125.0House10.010.010.010.097.010.0Entire home/aptNY550.010464
64.0{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","First aid kit","Safety card","Fire extinguisher",Essentials,Shampoo,"24-hour check-in",Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}151.0Real Bed1.02.0flexiblelong island cityNaN5671450411675715t40.851391-73.784139NYCCozy 1 BR Basement Apartment85.0House10.010.010.010.098.010.0Entire home/aptNYNaN10464
73.0{"Cable TV",Internet,"Wireless Internet","Air conditioning",Kitchen,"Family/kid friendly","Smoke detector","First aid kit","Fire extinguisher"}51.0Real Bed1.02.0strictsunnysidebronxNaN3684360715270f40.859559-73.870669NYC2 Beds/Queen & Full Beautiful Room 40 minsT.Square39.0Apartment9.09.09.09.090.09.0Private roomNYNaN10467
85.0{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises",Heating,"Smoke detector","Carbon monoxide detector",Essentials,Hangers,"Laptop friendly workspace","translation missing: en.hosting_amenity_49","translation missing: en.hosting_amenity_50","Self Check-In",Keypad,"Private entrance"}171.0Real Bed1.01.0moderatesunnysidebronxNaN1130594417876530f40.868682-73.854828NYCSpacious Garden Apartment95.0House10.010.010.010.0100.010.0Entire home/aptNYNaN10469
98.0{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,"Pets allowed",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","First aid kit","Safety card","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}121.0Real Bed1.03.0strictsunnysidebronxNaN873273182177t40.864658-73.857087NYCPRIVATE FLAT / APARTMENT- $SPECIAL$125.0Apartment10.09.010.09.092.09.0Entire home/aptNYNaN10469

Last rows

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598176.0{TV,"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector",Essentials,Shampoo}242.0Real Bed3.03.0flexiblehyattsvilleNaN12741146118393534t38.947872-76.975137dcHyattsville House - Seconds from D.C.240.0HouseNaNNaNNaNNaNNaNNaNEntire home/aptMDNaN20782
598182.0{TV,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises","Pets allowed",Heating,Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit",Essentials,"Lock on bedroom door",Hangers,"Hair dryer",Iron,"Laptop friendly workspace","Private living room"}281.0Real Bed1.01.0flexiblewashingtonNaN12783701918442194t38.855900-76.950383dcCozy Townhome37.0HouseNaNNaNNaNNaNNaNNaNPrivate roomDCNaN20020
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598222.0{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning",Pool,Kitchen,Doorman,Gym,"Elevator in building",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Fire extinguisher",Essentials,Shampoo,Hangers,Iron,"Laptop friendly workspace","Private entrance",Bathtub}121.0Real Bed1.01.0moderatesilver springNaN4107164918223756t38.991331-77.028024dcModern Condo 5 min from DC115.0Condominium10.09.010.010.0100.010.0Entire home/aptMDNaN20910
598232.0{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","First aid kit","Fire extinguisher",Shampoo,Hangers,"Hair dryer",Iron}241.0Real Bed1.01.0flexiblecapitol heightsNaN2340570918364166f38.885421-76.916581dcGreat PRV 1bd/1bath 2blocks to DC0.0HouseNaNNaNNaNNaNNaNNaNPrivate roomMDNaN20743